Method and apparatus for acoustic detection of a fluid leak behind a casing of a borehole

ABSTRACT

An acoustic detection of a discrete acoustic signal allows to detect a leak behind a casing ( 23 ) of a borehole. An acoustic amplitude (AA) is sampled during a recording time period ( 24;64 ) at a determined position along the tube. Time intervals ( 26;66 ) are defined inside of the recording time period ( 24 ), and for each time interval ( 26 ) the measured acoustic amplitudes are processed to obtain respectively a corresponding power-frequency spectrum ( 261 ). A plurality of the power-frequency spectra are analyzed to identify the discrete acoustic signal by detecting time and frequency dependant changes of power. Preferably the processing involves a Fourier transform analysis. A power-frequency time plot is used to conveniently identify the discrete acoustic event.

TECHNICAL FIELD

The invention relates to the field of acoustic investigation techniquesused in wells.

BACKGROUND OF THE INVENTION

Acoustic investigation techniques are widely used as non-destructivetools to check the integrity of wells and their casing. As an example,noise logging has been used for almost 30 years to detect the locationof gas leaks behind a casing of a well.

Gas leaks behind casing generally occur when a gas-bearing zone has notbeen properly isolated during the well construction process. The lack ofzonal isolation allows gas to flow from the gas-bearing zone to thesurface or to another subterranean zone outside the casing. The gas leakmay for example cause an uncontrolled accumulation of gas behind thecasing or at the surface of the well, and lead to a hazardous situationssuch as the contamination of a water table surrounding the well or thecreation of an explosive mixture of gas at the surface.

Turning now to FIG. 1, a schematic diagram illustrating a generalprinciple of the logging operation in a well is shown. A tool or sonde10, for acquiring noise data is located in a borehole 11 penetrating anearth formation 12. The borehole 11 is lined by a casing 13. The sonde10 is lowered in the borehole 11 by a cable 14 and slowly raised by asurface equipment 15 over a sheave wheel 16 while noise data isrecorded. A depth of the sonde 10 is measured using a depth gauge 17which measures cable displacement, Noise data acquired by the sonde 10may be analysed either in situ near the sonde 10, or analysed by a dataprocessor 18 at the surface, or stored, for later analysis.

Reliable detection of the position of a leak in the borehole is criticalin designing a repair job for that leak and for subsequent determinationof the success of leak repair.

Many techniques have been used to detect a position of a fluid leak in aborehole or in other environments. These techniques have been appliedeither individually or in combination with each other as will beunderstood from the prior art documents described hereafter.

One form of noise logging tool was proposed in the 1970's and isdescribed in more detail in the following references:

-   -   McKinley, R. M., Bower, F. M., and Rumble, R. C.: “The Structure        and Interpretation of Noise From Flow Behind Cemented Casing,”        SPE3999, JPT, March 1973, P329,    -   Robinson, W. S.: “Field Results From the Noise-Logging        Technique,” SPE5088, JPT, November 1976, P1370,    -   McKinley, and R. M., Bower, F. M.: “Specialized Applications of        Noise Logging,” SPE6784, JPT, March 1979, P1387,    -   Brift, E. L.: “Theory and Applications of the Borehole Audio        Tracer Survey,” SPE6552, Transactions of the SPWLA Seventeenth        Annual Logging Symposium, Jun. 9-12, 1976, Denver, Colo.,    -   Koerner, Jr., H. B., and Carroll, J. C.: “Use of the Noise Log        as a Downhole Diagnostic Tool,” SPE7774, presented at the SPE        Middle East Oil Technical Conference, Bahrain, 25-29 Mar. 1979.

The tool generally contains 4-6 high pass filters that transmit noiseamplitudes above 200, 600, 1000, 2000, 4000 and 6000 Hz. For tools withthe lowest number of filters, the 4000 and 6000 Hz cutoffs areeliminated. The noise data provided at surface is an average of thesemeasured transmitted noise amplitudes over a certain time period, 10seconds for example. The coarse frequency resolution and the timeaveraging limit the application of this type of tool to relatively highleak rates where the noise generated is semi-continuous and significantcompared to background noise.

SUMMARY OF THE INVENTION

In a first aspect the invention provides a method for acoustic detectionof a leak behind a casing of a borehole. The leak generates a discreteacoustic signal. The method comprises sampling an acoustic amplitudeduring a recording time period at a determined position along theborehole, and defining time intervals inside of the recording timeperiod. For each time Interval the measured acoustic amplitudes areprocessed to obtain respectively a corresponding power-frequencyspectrum. A plurality of the power-frequency spectra are analysed toidentify the discrete acoustic signal by detecting time and frequencydependant changes of power.

In a first preferred embodiment the processing is performed using aFourier transform analysis.

In a second preferred embodiment the time intervals are of same durationand subsequent time intervals are adjacent to each other in order tocover a continuous portion of the recording time period.

In a third preferred embodiment the method comprises plotting thepower-frequency spectra in a power-frequency-time plot graph, andidentifying a surface of the power-frequency-time plot graph wherein avalue of power corresponds to a predetermined value. The identifiedsurface is analysed to detect the discrete acoustic signal.

In a fourth preferred embodiment a duration of the recording time periodis adapted to measure at least one discrete acoustic signal.

In a fifth preferred embodiment the sampling is performed at one or aplurality of further determined positions along the borehole in order toinvestigate a section of the borehole covered by the determined andfurther determined positions.

In a sixth preferred embodiment the power-frequency-time plots resultingfrom the measured acoustic amplitudes are aligned into an extended graphin an order corresponding to successive positions of the borehole, theextended graph showing frequency and time dependant power values asoccurring along the borehole.

In a seventh preferred embodiment the sampling is done at an acquisitionrate between 30 kHz and 50 kHz.

In a second aspect the invention provides a method for detection of aleak behind a casing of a borehole. A portion of the borehole isinvestigated using a first investigation method to obtain a first resultof investigation. The portion of the borehole is also investigated usinga method for acoustic detection of a leak behind a casing to obtain asecond result of investigation. The first and the second results ofinvestigation are compared to identify a correlation between the firstand the second results.

In a third aspect the invention provides a method for repairing a leakwherein the leak is detected using a method for acoustic detection of aleak behind a casing of the borehole, and a repair process activated forrepairing the leak.

In an eighth preferred embodiment the repair process comprisesperforating the casing to obtain an opening, and squeezing a repairfluid in the opening.

In a ninth preferred embodiment the repair process comprises milling outthe casing around the leak and placing a plug of sealing fluid to coverat least an entire volume milled out from the casing.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in greater detail with reference tothe accompanying drawings, in which:

FIG. 1 illustrates a schematic diagram of a logging operation from priorart;

FIG. 2 illustrates a first example embodiment of measuring andprocessing acoustic amplitudes according to the invention;

FIG. 3 illustrates an example of a power-time-frequency plot accordingto the invention;

FIG. 4A illustrates two separate power-frequency spectra calculated atdifferent times from acoustic amplitudes measured in a same period ofrecording time;

FIG. 4B contains an example of acoustic amplitude recordings fromadjacent time intervals according to the invention;

FIG. 5 illustrates a further example of a power-time-frequency plotaccording to the invention;

FIG. 6 illustrates a second example embodiment for measuring andprocessing acoustic amplitudes according to the invention;

FIG. 7 illustrates an example of power-time-frequency plots presented asa function of depth according to the invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Same references will be used to reference the same elements in theFigures throughout the description.

General Overview

FIG. 2 provides an illustration of one example embodiment of the presentinvention. In this embodiment, passive noise recording is performed at agiven depth in a borehole 21. A suitable noise detector 22 (hydrophoneor geophone for example) is used to record Acoustic Amplitude (AA) for agiven period of recording time 24.

The acoustic amplitude is recorded during one or a plurality of periodsof recording time that generally have a duration adapted to be able tocapture at least one acoustic event generated by a leak. The duration ofthe period of recording time 24 may for example have a value in a rangefrom 10 to 30 seconds. The value may be decreased to 5 seconds or lessfor acoustic events that occur several times per second, and increasedto several minutes for acoustic events that occur two or three times perminute. It is important that the recording time is sufficient to allow arepresentative portion of noise to be recorded.

Recorded information is sent to a surface system 25 for acquisition andanalysis. In other examples of embodiments, the recorded information maybe processed and analysed in situ near the noise detector, or stored forfurther analysis.

Time intervals 26 are defined inside of the period of recording time 24.The acoustic amplitude measurements of a time interval 26 are processedto obtain a power-frequency (p, f) spectrum 261 for this time interval26. In this embodiment, Fourier transform analysis is performed overeach time interval 26, thus providing a plurality of power-frequencyspectra 261.

The acoustic amplitude is measured using a sampling method. The samplingmethod comprises acquiring measurements at a rate adapted to obtain adesired acoustic frequency range. As an example, the acquisition ratemay be between 30 and 50 kHz in order to allow the full audiblefrequency range to be analysed.

An optimum number of measured samples and, consequently, a length of thetime intervals 26 used for the Fourier transform analysis may be afunction of the acquisition rate. The number of measured samples shouldbe sufficient to obtain a suitable acoustic frequency resolution but nottoo great so as to avoid averaging data corresponding to the measuredsamples over a duration much longer than a possible duration of theacoustic event that would be generated by a leak.

A particularly useful acquisition rate is 44 kHz. This acquisition rateallows the entire audible frequency range (20-20000 Hz) to be covered.In this embodiment, Fourier transforms over 1024 or 2048 measurementsamples correspond to a time resolution of approximately 25 ms or 50 msrespectively.

The power-frequency spectra 261 are analysed to detect time andfrequency dependant changes of power.

In a preferred embodiment, the resulting data may be plotted in apower-frequency-time plot 27. The abscissa and the ordinate respectivelyare indicative of time and frequency f. A power density representativeof a value of the frequency dependent power may for example be indicatedin the plot 27 using colour. Any other method for characterizing asurface may be used instead as appropriate: various greyscales may beused to represent respective associated values of power density, forexample a dark grey may represent a high value while a lighter greywould indicate a lower value. In a similar manner shading of surfaces,or filling with patterns etc . . . may be used to obtain arepresentation of different values as appropriate. Returning to thediscussed example, a plurality of colours may indicate a plurality ofpower density values.

On the power-frequency-time plot 27 of FIG. 2, hatched surfaces 28 and29 are used to represent 2 power density values instead of 2 colours, inview of the black and white nature of the plot.

Power-Frequency-Time Plot

An example of a power-frequency-time plot is shown in FIG. 3. Hatchedsurfaces represent power densities having a value exceeding a determinedthreshold value, similar as in the power-frequency-time plot 27 of FIG.2.

The abscissa shows the time in seconds.

In this example the acoustic amplitude of the noise was recorded duringa period of 20 seconds at an acquisition rate of 44 kHz. Fouriertransform is performed for 1024 subsequent measured samples, i.e. formeasurements recorded in a time interval of approximately 25 ms.

The ordinate indicates frequency in kHz, from 0 to 11 kHz.

The values of the power density may for example be in dB, normalized toan arbitrary value.

The example from FIG. 3 is obtained using an experimental set-up asfollows: a gas is bubbled into a large container of water from an outletsized approximately 12 mm in diameter. The bubbles produced areapproximately 15 mm in diameter and generated at a rate of approximately3 bubbles every 2 seconds. A suitable noise detector containing apiezoelectric noise transducer and electronics is placed into the waterat a distance of approximately 20 cm from the bubbling gas outlet.Hatched surfaces 31 correspond to power density peaks of discreteacoustic events caused by the bubble formation.

Effect of Analysing power-Frequency Spectra for a Plurality ofSubsequent Time Intervals

FIG. 4A contains power-frequency spectra that Illustrate an advantage ofperforming Fourier transform analysis over measured samplescorresponding to subsequent short time intervals in a continuous periodof recording time.

A first power-frequency spectrum 41 (represented using a solid line) anda second power-frequency spectrum 42 (represented using a dotted line),distinct from the first spectrum 41, are generated using Fouriertransform analysis on measured samples covering the acoustic event 32shown in FIG. 3. The acoustic event 32 occurs approximately at a timet=2 s.

Referring to FIG. 4B, Acoustic Amplitude measurements for the acousticevent 32 of FIG. 3 are schematically illustrated. It is understood thatAcoustic Amplitude values illustrated in this example have been chosenarbitrarily and may not exactly correspond to the power-frequencyspectra 41 and 42. Each of the first spectrum 41 and the second spectrum42 is obtained from measurement samples recorded during time intervals43 and 44. Each of the time intervals 43 and 44 has a duration ofapproximately 25 ms and comprises 1024 measurement samples. In otherwords, the time intervals used to obtain the first spectrum 41 and thesecond spectrum 42 are of substantially the same length and areadjacent.

The first spectrum 41 shows an overall noisy appearance but does notreveal any outstanding power peak. The first spectrum 41 fails tocapture the acoustic event 32.

On the other hand, the second spectrum 42 shows a high power densitypeak located in the frequency range below 2 kHz corresponding to theacoustic event 32. Hence it appears important to use a plurality ofsubsequent time intervals from a continuous period of recording time,allowing to scan the period of recording time, in order to enable thedetection of a discrete acoustic event occurring during the period ofrecording time.

Averaging Effect

In case a Fourier transform analysis is performed over measurementsamples taken during a relatively long time interval, e.g. a set of 4096samples, which corresponds, at an acquisition rate of 44 kHz, to aduration substantially equal to 100 ms, high amplitude samplescorresponding to the acoustic event would have a smaller relativeweight. As a consequence, the high amplitude acoustic event may not bedetected so easily on a power-frequency spectrum calculated over samplescorresponding to a relatively long time interval. Performing Fouriertransforms on measured samples corresponding to a relatively long timeinterval may suffer an excessive averaging effect.

Effect of Varying a Duration of the Recording Time Period

FIG. 5 contains a first power-frequency-time plot in part A and a secondpower-frequency-time plot in part B.

A laboratory recording of gas bubbling into water is performed using asimilar experimental set-up as described hereinabove in relation tomeasurements shown in FIG. 3, except that the bubble rate is lower:approximately one bubble every four seconds.

The first power-frequency-time plot in part A results from a recordingtime period of approximately 3 s recorded between instants t1=2 s andt2=5 s. There is no power density value exceeding the threshold valueand hence no high amplitude acoustic signal visible over this timeperiod.

The second power-frequency-time plot in part B results from a recordingtime period of approximately 16 s recorded between instants t0=0 s andt3=16 s. A plurality of approximately regularly spaced (in time) hatchedsurfaces 51 representing power density values that exceed the thresholdvalue appear at approximately 2, 6, 10 and 14 seconds. The hatchedsurfaces 51 indicate a plurality of discrete acoustic events.

The difference between the first power-frequency-time plot and thesecond power-frequency time plot illustrates that it is necessary toadapt the duration of the recording time period to the rate of thebubble flowing from the gas outlet in order to have the discreteacoustic event from the bubble inside the recording time period.

Investigation of an Extended Borehole Length

FIG. 6 contains an illustration of another example embodiment of thepresent invention as used to investigate an extended borehole lengthcorresponding to a section lying between depths D₁ and D_(n).

It is understood that the depths are indications of positions in theborehole. A person skilled in the art may well understand that anextended borehole length corresponding to a section lying betweendetermined positions that do not necessarily correspond to depths may beinvestigated in a similar manner.

At least one passive noise recording is performed at each one of severalrecording depths D₁, D₂ . . . , D_(n) of a borehole 61, using a movablenoise detector 62.

A spacing between two adjacent recording depths, e.g between D₁ andD_(2,) may have an influence on the following parameters:

a reduction of the spacing may result in an overall increased timerequired to investigate a given section since the number of passiverecordings is also increased.

an increase of the spacing may in some cases result in failing to detectacoustic generating locations that are too distant from the noisedetector 62.

The noise detector 62, e.g. a hydrophone or a geophone, is used torecord acoustic amplitude downhole for each depth D₁, D₂ . . . , D_(n)respectively for a given period of recording time 64. Information fromthe measured Acoustic Amplitudes is sent to a surface system 65 foracquisition and analysis. In other examples of embodiments, the recordedinformation may be processed and analysed in situ near the noisedetector 62, or stored for further analysis.

In a similar way as described for measurements made in the system fromFIG. 2, time intervals 66 are defined inside of the period of recordingtime 64. For each time interval 66 the Acoustic Amplitudes are processedto obtain a corresponding power-frequency spectrum (not shown in FIG.6), using for example Fourier transform analysis.

Hence a plurality of power-frequency spectra is provided for each periodof recording time 64, i.e. for each recording depth D₁, D₂ . . . ,D_(n).

The parameters such as for example the duration of the period ofrecording time 64, the acquisition rate, and the number of measuredsamples over which the Fourier transform is performed may be adjusted asdiscussed in relation to measurements discussed with FIGS. 2-5.

The power-frequency spectra are analysed to detect time and frequencydependant changes of power characteristic of discrete acoustic events.In the illustrated example, the power frequency data for each depth D₁,D₂ . . . , D_(n) may respectively be plotted in a power-frequency-timeplot PFT_(l) for the depth D_(l).

The power-frequency-time plots PFT₁, . . . ,PFT_(n) may be graphicallyassembled in an extended plot, by juxtaposing the plots one after theother for easier analysis as a function of the depth as shown in FIG. 7.

The example shown in FIG. 7 is the result of acoustic recordingsperformed at various relative depths, i.e. at 10, 20, 23, 24, 25, 26, 27and 30 m. These values do not necessarily represent the actual depth asmeasured from the surface. These values are indicators of distancerelative to a determined depth and measured along the section of theborehole. At each depth, Acoustic Amplitude is recorded during a periodof recording time of 20 seconds at an acquisition rate of 44 kHz.

For each depth a power-frequency-time plot is generated. A part of eachpower-frequency-time plot corresponding to a duration of 4 s ispresented graphically as a function of depth. In this example, the 4 spart of the plot corresponding to measurements made between instantsTB=0 s and TE=4 s of the respective periods of recording time, revealsitself to be adapted to detect an acoustic event occurring in thatperiod during measurement.

Hatched surfaces in the plots correspond to power density valuesexceeding the threshold. The plots at 10 m, 20 m, 23 m and 30 m reveal 3frequency ranges centred at values of approximately 1.8, 4 and 7 kHz,wherein power density values exceed the threshold value, indicatingsignificant background noise at these frequencies. A source of acousticevents at the depth of 25 m may be identified by an increasing hatchedsurface appearing on the corresponding plot. An attenuation of theacoustic signal away from the source is indicated in the neighbouringplots corresponding to the depths of 24 m, 26 m and 27 m wherein hatchedsurfaces indicating higher frequency portions of the spectra are reducedor disappear as compared to the 25 m plot.

The overall plot format provides a convenient means to identify thelocation of acoustic events, even to an inexperienced eye, in that itspresentation of the results draws the eye to the relatively “noisy”sections of the logged interval.

It may be noted that the significant background noise in this example iscaused by a wire line truck at surface.

In a preferred embodiment the acoustic recordings are performed with thetool stationary at various places in the borehole. This allows to reducenoise that is due purely to the movement of the tool in the borehole.

The inventive method for acoustic detection of a leak may be combinedwith another commonly used logging method to assess the integrity of theborehole. It may for example be combined with ultrasonic logging. Theresults obtained using the acoustic detection of a leak and the othermethod, e.g. the ultrasonic logging method, may be compared andcorrelated to confirm conclusions on the existence of a leak in theborehole.

The identification of a leak behind a casing may be followed by repairof the leak. In one example of such a repair, a repairing tool ispositioned in proximity of the noise source. Every time a leak isidentified, the repair tool perforates the casing to obtain an openingand subsequently squeezes in a repair fluid in the opening. The repairfluid may for example be micro-cement, resin or other. In anotherexample embodiment, the repair tool may mill out the casing around theidentified leak, and subsequently place a plug of sealing fluid to coverat least the entire volume milled out across from one formation wall toanother.

While the invention has been described with respect to a limited numberof embodiments, a person skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

1. A method for acoustic detection of a leak behind a casing (23) of aborehole, the leak generating a discrete acoustic signal, the methodcomprising sampling an acoustic amplitude (AA) during a recording timeperiod (24;64) at a determined position along the borehole, definingtime intervals (26;66) inside of the recording time period (24),processing for each time interval (26) the measured acoustic amplitudesto obtain respectively a corresponding power-frequency spectrum (261),analysing a plurality of the power-frequency spectra to identify thediscrete acoustic signal by detecting time and frequency dependantchanges of power.
 2. The method of claim 1, wherein the processing isperformed using a Fourier transform analysis
 3. The method according toclaim 1, wherein the time intervals are of same duration and subsequenttime intervals are adjacent to each other in order to cover a continuousportion of the recording time period.
 4. The method according to claim1, further comprising plotting the power-frequency spectra in apower-frequency-time plot graph (27; PFT1, PFT2, . . . , PFTn),identifying a surface (28, 29;31;51) of the power-frequency-time plotgraph wherein a value of power corresponds to a predetermined value,analysing the identified surface to detect the discrete acoustic signal.5. The method according to claim 1, wherein a duration of the recordingtime period is adapted to measure at least one discrete acoustic signal.6. The method according to claim 1, wherein the sampling is performed atone or a plurality of further determined positions along the borehole inorder to investigate a section of the borehole covered by the determinedand further determined positions.
 7. The method according to claim 4,wherein the sampling is performed at one or a plurality of furtherdetermined positions along the borehole in order to investigate asection of the borehole covered by the determined and further determinedpositions, and wherein the power-frequency-time plots resulting from themeasured acoustic amplitudes are aligned into an extended graph in anorder corresponding to successive positions of the borehole, theextended graph showing frequency and time dependant power values asoccurring along the borehole.
 8. The method according to claim 1,wherein the sampling is done at an acquisition rate between 30 kHz and50 kHz.
 9. (canceled)
 10. (canceled)
 11. The method for repairing a leakaccording to claim 14, wherein the repair process comprises perforatingthe casing to obtain an opening and squeezing a repair fluid in theopening.
 12. The method for repairing a leak according to claim 14,wherein the repair process comprises milling out the casing around theleak and placing a plug of sealing fluid to cover at least an entirevolume milled out from the casing.
 13. A method for detection of a leakbehind a casing of a borehole, comprising investigating a portion of theborehole using a first investigation method to obtain a first result ofinvestigation, investigating the portion of the borehole to obtain asecond result of investigation using a method for acoustic detection ofa leak behind a casing (23) of a borehole, the leak generating adiscrete acoustic signal, the method for acoustic detection comprising:sampling an acoustic amplitude (AA) during a recording time period(24;64) at a determined position along the borehole, defining timeintervals (26;66) inside of the recording time period (24), processingfor each time interval (26) the measured acoustic amplitudes to obtainrespectively a corresponding power-frequency spectrum (261), analysing aplurality of the power-frequency spectra to identify the discreteacoustic signal by detecting time and frequency dependant changes ofpower; comparing the first result of investigation with the secondresult of investigation to identify a correlation between the firstresult and the second result.
 14. A method for repairing a leak behind acasing of a borehole, comprising: Detecting the leak using a method foracoustic detection of a leak behind a casing (23) of a borehole, theleak generating a discrete acoustic signal, the method comprising: i.sampling an acoustic amplitude (AA) during a recording time period(24;64) at a determined position along the borehole, ii. defining timeintervals (26;66) inside of the recording time period (24); iii.processing for each time interval (26) the measured acoustic amplitudesto obtain respectively a corresponding power-frequency spectrum (261);iv. analysing a plurality of the power-frequency spectra to identify thediscrete acoustic signal by detecting time and frequency dependantchanges of power; Activating a repair process for repairing the leak.